ir_aggregate_isofiles() *
# aggregate the data
dataset <- isofiles |> ir_aggregate_isofiles()
✔ [75ms] ir_aggregate_isofiles() aggregated metadata (2) and traces (24.5k,
intensity in mV) from 2 files using the standard aggregator
# show all that was recovered
# (as well as what was ignored / not aggregated)
dataset
─ aggregated data from 2 isofiles with 2 analyses - retrieve with ir_get_data( ─
→ metadata (2): uidx, file_path, file_name, analysis, timestamp, type,
h3_factor (1 NA), Row (1 NA), Peak Center (1 NA), Check Ref. Dilution (1 NA),
H3 Stability (1 NA), H3 Factor (1 NA), Amount (1 NA), Type (1 NA), EA Method (1
NA), Identifier 1, Identifier 2 (1 NA), Analysis, Comment (1 NA), Preparation
(1 NA), Method, Line (1 NA), GC Method (1 NA), AS Sample (1 NA), AS Method (1
NA), Pre Script (all NA), Post Script (all NA)
→ traces (24.5k): uidx, analysis, species, mass, tp, time.s, intensity.mV; (not
aggregated: channel)
→ problems: has no issues
bonus intensity units
By default the raw signals are reported in millivolts
(mV). Pass a different intensity_units to
report voltage (mV, V), current
(fA, pA, nA, µA,
mA, A), or counts per second
(cps) instead. Conversions that cross the
voltage/current/cps boundary use the recorded resistor values
automatically (the same machinery is exposed as
ir_convert_intensity()).
# report intensities in nanoamperes instead of the default millivolts
isofiles |>
ir_aggregate_isofiles(intensity_units = "nA") |>
ir_get_data(traces = c("species", "mass", "time.s", "intensity.nA"))
✔ [76ms] ir_aggregate_isofiles() aggregated metadata (2) and traces (24.5k,
intensity in nA) from 2 files using the standard aggregator
✔ [3ms] ir_get_data() retrieved 24.5k records from the combination of metadata
(2) and traces (24.5k) via uidx and analysis
# A tibble: 24,515 × 7
uidx analysis file_name species mass time.s intensity.nA
<int> <int> <chr> <chr> <chr> <dbl> <dbl>
1 1 1 continuous_flow_ea_example N2 28 0.209 0.0705
2 1 1 continuous_flow_ea_example N2 28 0.418 0.0704
3 1 1 continuous_flow_ea_example N2 28 0.627 0.0704
4 1 1 continuous_flow_ea_example N2 28 0.836 0.0704
5 1 1 continuous_flow_ea_example N2 28 1.04 0.0703
6 1 1 continuous_flow_ea_example N2 28 1.25 0.0703
7 1 1 continuous_flow_ea_example N2 28 1.46 0.0703
8 1 1 continuous_flow_ea_example N2 28 1.67 0.0702
9 1 1 continuous_flow_ea_example N2 28 1.88 0.0702
10 1 1 continuous_flow_ea_example N2 28 2.09 0.0701
# ℹ 24,505 more rows
bonus ir_get_aggregator()
You can optionally use a different aggregator. Besides the default
standard aggregator, the package ships (from smallest to
largest) the metadata aggregator (metadata only), the
minimal aggregator (metadata plus the core data series),
and the extended aggregator, which is more elaborate and
provides access to additional columns (such as the resistor/cup
configuration and the vendor data table) from the data files.
# minimal vs. extended aggregator
ir_get_aggregator("minimal")
────────────────────────────── Aggregator minimal ──────────────────────────────
Dataset metadata:
→ file_name = as.character(sub(file_name, pattern = "\\.[^.]+$", replacement =
""))
→ analysis = as.integer(analysis)
→ timestamp = as.POSIXct(timestamp)
Dataset traces:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ mass = as.character(mass)
→ tp = as.integer(tp)
→ time.s = as.numeric(time.s)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
Dataset cycles:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ cycle = as.integer(cycle)
→ type = as.character(type)
→ mass = as.character(mass)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
Dataset scans:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ mass = as.character(mass)
→ x = as.numeric(x)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
ir_get_aggregator("extended")
────────────────────────────── Aggregator extended ─────────────────────────────
Dataset metadata:
→ file_name = as.character(sub(file_name, pattern = "\\.[^.]+$", replacement =
""))
→ analysis = as.integer(analysis)
→ timestamp = as.POSIXct(timestamp)
→ (.*) = as.character(all_matches("(.*)"))
Dataset traces:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ mass = as.character(mass)
→ tp = as.integer(tp)
→ time.s = as.numeric(time.s)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
→ channel = as.integer(channel)
→ config = as.integer(config)
Dataset cycles:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ cycle = as.integer(cycle)
→ type = as.character(type)
→ mass = as.character(mass)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
→ channel = as.integer(channel)
Dataset scans:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ mass = as.character(mass)
→ x = as.numeric(x)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
→ channel = as.integer(channel)
Dataset resistors:
→ species = as.character(species)
→ config = as.integer(config)
→ channel = as.integer(channel)
→ mass = as.character(mass)
→ cup = as.integer(cup)
→ resistance.Ohm = as.numeric(resistance.Ohm)
→ nominal.Ohm = as.numeric(nominal.Ohm)
Dataset vendor_data_table:
→ (.*) = keep_as_is(all_matches("(.*)"))
# using the extended aggregator instead of the default (standard)
isofiles |> ir_aggregate_isofiles(aggregator = "extended")
✔ [165ms] ir_aggregate_isofiles() aggregated metadata (2), traces (24.5k,
intensity in mV), resistors (8), and vendor_data_table (25) from 2 files using
the extended aggregator
─ aggregated data from 2 isofiles with 2 analyses - retrieve with ir_get_data( ─
→ metadata (2): uidx, file_path, file_name, analysis, timestamp, type,
h3_factor (1 NA), Row (1 NA), Peak Center (1 NA), Check Ref. Dilution (1 NA),
H3 Stability (1 NA), H3 Factor (1 NA), Amount (1 NA), Type (1 NA), EA Method (1
NA), Identifier 1, Identifier 2 (1 NA), Analysis, Comment (1 NA), Preparation
(1 NA), Method, Line (1 NA), GC Method (1 NA), AS Sample (1 NA), AS Method (1
NA), Pre Script (all NA), Post Script (all NA)
→ traces (24.5k): uidx, analysis, species, mass, tp, time.s, intensity.mV,
channel, config (all NA)
→ resistors (8): uidx, species, config (all NA), channel, mass, cup,
resistance.Ohm, nominal.Ohm (all NA)
→ vendor_data_table (25): uidx, analysis, species, Start [s], Rt [s], End [s],
Ampl 28 [mV] (22 NA), Ampl 29 [mV] (22 NA), Ampl 30 [mV] (22 NA), BGD 28 [mV]
(22 NA), BGD 29 [mV] (22 NA), BGD 30 [mV] (22 NA), Nr., rIntensity 28 [mVs] (22
NA), rIntensity 29 [mVs] (22 NA), rIntensity 30 [mVs] (22 NA), rIntensity All
[mVs], Intensity 28 [Vs] (22 NA), Intensity 29 [Vs] (22 NA), Intensity 30 [Vs]
(22 NA), Intensity All [Vs], Sample Dilution [%] (19 NA), List First Peak (22
NA), rR 29N2/28N2 (22 NA), Is Ref.?, R 29N2/28N2 (22 NA), Ref. Name, rd
29N2/28N2 [per mil] (22 NA), d 29N2/28N2 [per mil] (22 NA), R 15N/14N (22 NA),
δ 15N/14N [per mil] (22 NA), AT% 15N/14N [%] (22 NA), Rps 29N2/28N2 (24 NA),
Ampl 44 [mV] (22 NA), Ampl 45 [mV] (22 NA), Ampl 46 [mV] (22 NA), BGD 44 [mV]
(22 NA), BGD 45 [mV] (22 NA), BGD 46 [mV] (22 NA), rIntensity 44 [mVs] (22 NA),
rIntensity 45 [mVs] (22 NA), rIntensity 46 [mVs] (22 NA), Intensity 44 [Vs] (22
NA), Intensity 45 [Vs] (22 NA), Intensity 46 [Vs] (22 NA), rR 45CO2/44CO2 (22
NA), rR 46CO2/44CO2 (22 NA), R 45CO2/44CO2 (22 NA), rd 45CO2/44CO2 [per mil]
(22 NA), d 45CO2/44CO2 [per mil] (22 NA), R 46CO2/44CO2 (22 NA), rd 46CO2/44CO2
[per mil] (22 NA), d 46CO2/44CO2 [per mil] (22 NA), R 13C/12C (22 NA), δ
13C/12C [per mil] (22 NA), AT% 13C/12C [%] (22 NA), R 18O/16O (22 NA), δ
18O/16O [per mil] (22 NA), AT% 18O/16O [%] (22 NA), R 17O/16O (22 NA), d
17O/16O (22 NA), Rps 45CO2/44CO2 (24 NA), Rps 46CO2/44CO2 (24 NA), Ampl 2 [mV]
(6 NA), Ampl 3 [mV] (6 NA), BGD 2 [mV] (6 NA), BGD 3 [mV] (6 NA), rIntensity 2
[mVs] (6 NA), rIntensity 3 [mVs] (6 NA), Intensity 2 [Vs] (6 NA), Intensity 3
[Vs] (6 NA), rR 3H2/2H2 (6 NA), R 3H2/2H2 (6 NA), rd 3H2/2H2 [per mil] (6 NA),
d 3H2/2H2 [per mil] (6 NA), R 2H/1H (6 NA), δ 2H/1H [per mil] (6 NA), AT% 2H/1H
[%] (6 NA), Rps 3H2/2H2 (20 NA)
→ problems: has no issues
bonus ir_register_aggregator()
Or build your own aggregator with ir_start_aggregator()
and/or expand an existing one with ir_add_to_aggregator(),
then register it via ir_register_aggregator(). This
functionality is rarely needed and thus not part of the package
structure flowchart.
my_agg <-
ir_get_aggregator("minimal") |>
# pull the "Identifier 1" metadata field out under a friendlier name
ir_add_to_aggregator("metadata", "sample_id", source = "Identifier 1") |>
ir_register_aggregator(name = "my_aggregator")
# show my aggregator summary
my_agg
────────────────────────────── Aggregator minimal ──────────────────────────────
Dataset metadata:
→ file_name = as.character(sub(file_name, pattern = "\\.[^.]+$", replacement =
""))
→ analysis = as.integer(analysis)
→ timestamp = as.POSIXct(timestamp)
→ sample_id = as.character(`Identifier 1`)
Dataset traces:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ mass = as.character(mass)
→ tp = as.integer(tp)
→ time.s = as.numeric(time.s)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
Dataset cycles:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ cycle = as.integer(cycle)
→ type = as.character(type)
→ mass = as.character(mass)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
Dataset scans:
→ analysis = as.integer(analysis)
→ species = as.character(species)
→ mass = as.character(mass)
→ x = as.numeric(x)
→ (intensity\\..*) = as.numeric(all_matches("(intensity\\..*)"))
# use it
isofiles |> ir_aggregate_isofiles(aggregator = "my_aggregator")
✔ [46ms] ir_aggregate_isofiles() aggregated metadata (2) and traces (24.5k,
intensity in mV) from 2 files using the my_aggregator aggregator
─ aggregated data from 2 isofiles with 2 analyses - retrieve with ir_get_data( ─
→ metadata (2): uidx, file_path, file_name, analysis, timestamp, sample_id;
(not aggregated: file_path, type, h3_factor, Row, Peak Center, Check Ref.
Dilution, H3 Stability, H3 Factor, Amount, Type, EA Method, Identifier 2,
Analysis, Comment, Preparation, Method, Line, GC Method, AS Sample, AS Method,
Pre Script, Post Script)
→ traces (24.5k): uidx, analysis, species, mass, tp, time.s, intensity.mV; (not
aggregated: channel)
→ problems: has no issues
bonus ir_get_problems() /
ir_show_problems()
Reading and aggregation are designed to be fail-safe: instead of
stopping at the first error, problems are collected and reported. There
were no problems with these example files so the result is empty, but
this can be very helpful for figuring out what went wrong.
ir_get_problems() returns the problems as a data frame
for further inspection:
isofiles |> ir_get_problems()
# A tibble: 0 × 6
# ℹ 6 variables: uidx <int>, file <chr>, type <chr>, call <chr>, message <chr>,
# condition <list>
dataset |> ir_get_problems()
# A tibble: 0 × 6
# ℹ 6 variables: uidx <int>, file <chr>, type <chr>, call <chr>, message <chr>,
# condition <list>
ir_show_problems() instead just prints out all the
problems directly:
isofiles |> ir_show_problems()
dataset |> ir_show_problems()
